Orthogonal projection to latent structures combined with artificial neural networks in non-destructive analysis of ebastine powder

Abstract

A new method orthogonal projection to latent structures (O-PLS) combined with artificial neural networks is investigated for non-destructive determination of ebastine powder via near-infrared (NIR) spectroscopy. The modern NIR spectroscopy is efficient, simple and non-destructive technique, which has been used in chemical analysis in diverse fields. Being a preprocessing method, O-PLS provides a way to remove systematic variation from an input data set X not correlated to the response set Y, and does not disturb the correlation between X and Y. In this paper, O-PLS pretreated spectral data was applied to establish the ANN model of ebastine powder, in this model, the concentration of ebastine as the active component was determined. The degree of approximation was employed as the selective criterion of the optimum network parameters. In order to compare the OPLS-ANN model, the calibration models that use first-derivative and second-derivative preprocessing spectra were also designed. Experimental results showed that the OPLS-ANN model was the best.